1. Field of the Invention
This invention relates to the field of biometrics and, more particularly, to the field of fusing multi-modal biometrics.
2. Discussion of the Related Art
Biometrics is the study of using intrinsic physical characteristics of a human being to identify or verify the identity of an individual human. Examples of these physical or biometric characteristics include fingerprints (more generally, friction ridges), speaker (voice) identification, iris matching, hand geometry or hand vein comparison, and DNA testing. Each specific biometric measurement type is typically termed a “modality”. Multi-biometric fusion is the action of combining the results of multiple measurements of the same (e.g., left and right iris) or differing (e.g., thumbprint, iris, and voice patterns) modalities to increase the identification performance of a biometric identification or verification system over that of a single measurement or single modality system.
One problem with combining biometric information from multiple sources is that typically the scoring methods and units used with a one type of biometric sample differ from that of another. For example, a device used for scanning irises for identity verification might compare a particular scanned iris with a database of known identified irises and generate a “match/no match” score of 70 on a scale of 1-100, with a higher number indicating a higher likelihood of there being a match. Another sample, e.g., a fingerprint sample taken by a particular device, may generate a match/no match score of 0.9, on a scale of 0-1, with the higher number indicating a higher likelihood of a match. Since different units and different scales may be used, difficulties obviously arise when trying to combine the data into a meaningful and useful result.
Current multi-biometric systems tend to treat each modality equally using simple score summing or binary decision-level fusion, and these systems have error rates that make large-scale operation difficult. Even a 1/1000 chance of an incorrect identity results in thousands of erroneous matches when querying databases with millions of entries. This can be particularly troublesome in high security applications that require extremely high precision (e.g., a false alarm rate less than one percent) (See A. K. Jain, S. Prabhakar, and S. Chen, Combining multiple matchers for a high security fingerprint verification system, Pattern Recognition Letters 20 (1999) 1371-1379).
To address the high-precision needs of high security applications, fusing multiple biometrics has been considered to help lower error rates (See A. Ross, A. K. Jain, Information Fusion in Biometrics, Pattern Recognition Letters 24, (2003) 2115-2125). Some fingerprint systems, for example, use all available impressions sequentially until an acceptable match is obtained, while others include logical (AND, OR) operations, summation of similarity scores, or can be viewed as a consensus of experts. More sophisticated methods have been considered for combining scores from separate classifiers for each biometric modality using different feature extraction and matching algorithms to generate their scores (Jain et al., supra; J. Kittler, et al., On combining classifiers, IEEE Trans. Pattern Anal. and Mach. Intell., 20(3) (1998) 226-239; J. Bigun, et al., Multimodal Biometric Authentication using Quality Signals in Mobile Communications, in: Proc. of IAPR Intl. Conf. on Image Analysis and Processing (ICIAP), IEEE CS Press, (2003) 2-13).
Recently, Rukhin and Malioutov (A. L. Rukhin, and I. Malioutov, Fusion of biometric algorithms in the recognition problem, Pattern Recognition Letters, 26 (2005) 679-684) proposed fusion based on a minimum distance method for combining rankings from several biometric algorithms. Researchers have also developed quality metrics which are often used as weights for optimally combining scores generated by multiple classifiers (J. Fierrez-Aguilar, et al., Kernel-based multimodal biometric verification using quality signals, in: Biometric Technology for Human Identification, Jain, A. K., and N. K. Ratha (Eds.), Proc. SPIE Vol. 5404, 2004, pp. 544-554; E. Tabassi, C. Wilson, C. Watson, Fingerprint Image Quality, Technical Report 7151, 2004; Bigun et al., supra; Y. Chen, S. Dass, and A. J. Jain, Fingerprint Quality Indices for Predicting Authentication Performance, AVBPA 2005, LNCS 3546 (2005) 160-170; L. M. Wein, and M. Baveja, Using Fingerprint Image Quality To Improve The Identification Performance Of The U.S. Visitor And Immigrant Status Indicator Technology Program, Proceedings of the National Academy of Science, 102(21) (2005) 7772-7775).
While each of the above techniques provide some beneficial results, they do not effectively utilize all of the information available to a score-level fusion system, including score values, quality estimates, and score distribution statistics.